CAREER: Organizational Adaptation in Artificial Agent Societies

这项研究的总体目标是开发在人工代理人社会中进行组织适应的方法，从而导致社会结构的短期和长期变化，从而导致明显的绩效改进。具体地说，该项目将开发技术，以在当地调整代理人之间的联系，并形成长期稳定的团队，从而产生反应迅速、有效的代理人社会。一个密切相关的教育目标是以要开发的组织学习软件为中心开发课程材料。多智能体系统的组织结构是指智能体之间的物理或虚拟连接的性质，包括它们之间的通信、熟悉度以及信任和声誉关系。代理可以通过修改连接、改变与其他代理的交互模式以及建立权威关系和分包合同来适应这种组织。有效的组织适应要求代理保持对与其连接的其他代理的知识，包括他们的能力、能力、资源能力、可靠性和可信性。从系统设计者的角度来看，制定协议和方法，使代理人能够适应他们自己的组织，需要理解组织变化如何在个人和全球层面影响系统动态。该项目将在模拟的多主体社会中开发一个组织适应的理论框架，并在一个实验试验台上实施该框架，并使用该框架开发两种形式的组织学习技术：网络结构的局部适应和形成稳定团队和联盟的基于合同的方法。这些技术将被应用于几个多智能体应用：多机器人探索、分布式车辆监控和跟踪以及供应链管理。具有不同程度自主性的软件代理是当前许多研究项目的重点。它们目前被用于信息收集、电子商务、虚拟娱乐和移动机器人应用。随着智能代理变得更加无处不在，如果由此产生的“代理社会”能够有效地工作，为其用户提供价值，这将是非常有益的。这项研究将在代理社会的表示、建模和自组织环境和协议方面取得根本性进展。这项工作的一个主要教育目标是分发软件和基准，以促进关于多主体组织适应的教育和研究。这一分配将包括一套适合课堂作业或独立研究项目的“微型项目”。其他教育目标包括向主要是本科生机构中代表性不足的学生提供服务，以及参与人工智能社区博士生的指导项目。

The overall goal of this research is to develop methods for organizational adaptation in artificial agent societies, resulting in short-term and long-term changes to the society's structure that lead to demonstrable performance improvements. Specifically, the project will develop techniques to locally adjust connections between agents and to form long-term stable teams, resulting in responsive, effective agent societies. A closely related educational objective is to develop course materials centered around the organizational learning software to be developed. The organizational structure of a multi-agent system refers to the nature of the physical or virtual connections among agents, including their communication, familiarity, and trust and reputation relationships. Agents can adapt this organization by modifying connections, by changing their patterns of interaction with other agents, and by establishing authority relationships and subcontracts. Effective organizational adaptation requires the agents to maintain knowledge of the other agents to whom they are connected, including their capabilities, competence, resource capacities, reliability, and trustworthiness. From the system designer's perspective, developing protocols and methods by which agents can adapt their own organization requires an understanding of how organizational change affects the system dynamics at an individual and at a global level. This project will develop a theoretical framework for organizational adaptation in a simulated multi-agent society, implement this framework within an experimental testbed, and use the framework to develop techniques for two forms of organizational learning: local adaptation of network structure and contract-based approaches for forming stable teams and coalitions. These techniques will be applied to several multi-agent applications: multi-robot exploration, distributed vehicle monitoring and tracking, and supply chain management. Software agents with varying degrees of autonomy are the focus of many current research projects. They are currently used for information gathering, e commerce, virtual entertainment, and mobile robot applications. As intelligent agents become more ubiquitous, it will be of great benefit if the resulting "agent societies" can work effectively to provide value to their users. This research will result in fundamental advances in representations, modeling, and self-organizing environments and protocols for agent societies. A primary educational objective of the work is to distribute software and benchmarks to facilitate education and research on multi-agent organizational adaptation. This distribution will include a suite of "mini-projects" suitable for classroom assignments or independent study research projects. The other educational objectives include outreach to underrepresented students at primarily undergraduate institutions and involvement in mentoring programs for doctoral students in the artificial intelligence community.